CN113311079B - Marker for thyroid cancer diagnosis, stratification and prognosis and application thereof - Google Patents
Marker for thyroid cancer diagnosis, stratification and prognosis and application thereof Download PDFInfo
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Abstract
The invention relates to the technical field of medical detection, in particular to a marker for thyroid cancer diagnosis, stratification and prognosis and application thereof. The present invention provides markers for thyroid nodules comprising a combination of one or more of the following N-sugar chain markers: CFa, A2Fa, A4G, A4S, A4F0S, A4L, A4E, A4F 0E. The present invention also provides a marker for thyroid cancer, which comprises a combination of one or more of the following N-sugar chain markers: CFa, A2Fa, A2L, A2 GL. The expression level of the N-sugar chain marker provided by the invention has obvious difference in healthy human bodies, thyroid nodules, thyroid cancer and thyroid cancer cervical lymph node metastasis patients, can be used for diagnosing thyroid nodules, judging benign and malignant thyroid tumors and judging thyroid cancer cervical lymph node metastasis, and has higher specificity, sensitivity and accuracy.
Description
Technical Field
The invention relates to the technical field of medical detection, in particular to a marker for thyroid cancer diagnosis, stratification and prognosis and application thereof.
Background
Thyroid Cancer (TC) is the most common malignancy of the endocrine system. In recent years, the incidence of the disease has increased remarkably. Thyroid cancer is mainly classified into four pathological types, and more than 90% of thyroid malignant tumors are thyroid papillary carcinomas (PTCs). Epidemiological studies have shown that the palpation rate of thyroid nodules in women is about 5% and in men about 1% in the population. About 5-15% of patients with nodules will have malignant lesions and need to be operated or treated in time, and the rest of the benign nodules need to be followed up in a standardized way. In addition, although the overall prognosis of differentiated thyroid cancer mainly based on PTC is good, the proportion of lesions obviously invading peripheral important structures is 6% -13% and the proportion of distant metastasis is 5.9% -23% at the time of definite diagnosis, and the 10-year survival rate is obviously reduced (26% -70%). And relevant literature reports show that the survival rate of malignant tumor patients can be remarkably improved by early diagnosis and timely treatment. In summary, the judgment of thyroid nodules and the early diagnosis of thyroid cancer are one of the key clinical problems.
Currently, there is no diagnostic marker for thyroid cancer in the clinic. Ultrasound and ultrasound-guided puncture, widely used for screening and diagnosis of thyroid nodules, is an important means for preoperatively assessing the benign and malignant nodules, but has significant disadvantages or limitations, such as: the method has the advantages of strong operation expertise, dependence on the experience of clinicians, invasiveness, reduced accuracy of judging the micro-nodules, uncertainty of cytological results of 20-30% of the nodules and the like. On the other hand, PTC is easy to cause cervical lymph node metastasis (30-80%), which causes the recurrence rate of local tumors to rise by 10-42%, and the death rate of patients also rises. Surgery is the primary treatment modality for PTC. The surgical procedure includes a thyroidectomy, while regional cervical lymph node clearing is performed if there is cervical lymph node metastasis, to avoid subsequent local tumor recurrence in the patient due to cervical lymph node metastasis. Determining whether a patient has lymph node metastasis before surgery is another key problem faced by thyroid cancer in the clinic. The current clinical diagnosis for determining the presence or absence of metastatic cancer in cervical lymph nodes is not accurate: preoperative ultrasound has high specificity but low sensitivity for judging cervical lymph node metastatic cancer; the intraoperative frozen pathology can also give clinical suggestion, but the false negative is high, the paraffin pathology of a patient evaluated as cervical lymph node metastatic cancer negative before an operation proves that the incidence rate of lymph node metastatic cancer is up to 30%, and the operation time is inevitably prolonged while waiting for the intraoperative pathology. Because many patients have occult metastasis, and the limitation of the technical means, it is difficult to accurately judge whether the lymph node metastasis exists in the patients before the operation. There is also controversy as to whether PTC is routinely used for prophylactic cervical lymph node dissection. Therefore, the clinician often needs to judge whether to perform cervical lymph node cleaning based on clinical criteria and personal experience in order to ensure that the treatment is thoroughly avoided from local recurrence, and meanwhile, to save the treatment cost and avoid side damage caused by over-treatment. Therefore, a more accurate and noninvasive thyroid cancer diagnosis, stratification and prognosis index is sought, and the method has important significance for clinical decision and patients.
Glycosylation is one of the most common and important post-translational modifications of proteins. Glycoprotein glycosylation is involved in many key physiological and pathological processes such as carcinogenesis, cancer progression, and cancer metastasis. Since sugar chains are involved in various processes associated with cancer (cell differentiation, adhesion, invasion, metastasis, cell signaling, etc.), abnormal glycosylation is considered as one of the hallmarks of cancer. A plurality of tumor markers clinically applied, such as CA125 (applied to ovarian cancer, endometrial cancer and the like), CA19-9 (applied to pancreatic cancer, esophageal cancer and the like), and the like are modified by sugar chains. The AFP kit for glycosylation (fucosylation) was approved by FDA in the united states in 2005 for clinical diagnosis of liver cancer. In addition to the possible changes in glycosylation of glycoproteins of cancer cell origin, the glycosylation of immunoglobulins (Igs) produced by B lymphocytes and of acute phase proteins synthesized by liver cells is also altered, indicating that the alteration in glycosylation is a consequence of the body's systemic response to "tumorigenesis". Importantly, this change is detectable in the body fluid. Thus, sugar chains are potential biomarkers in the blood circulation of cancer patients associated with systemic disorders. Therefore, a new way is provided for the discovery of the noninvasive serum tumor marker by analyzing the glycosylation spectrum of related glycoprotein in body fluid.
Disclosure of Invention
The object of the present invention is to provide a marker for thyroid cancer. The marker has higher specificity, sensitivity and accuracy when being used for thyroid cancer diagnosis, stratification and prognosis diagnosis. Another object of the invention is to provide products containing the marker and uses thereof.
The N-sugar chain marker for realizing specific, sensitive and accurate diagnosis of thyroid nodule, thyroid cancer and cervical lymph node metastasis thereof is obtained by detecting and analyzing a large number of healthy patients, thyroid nodule patients and thyroid cancer patients. By detecting the expression of the N-sugar chain markers in body fluid, the thyroid tumor can be judged to be benign or malignant, the thyroid cancer can be diagnosed at an early stage, and the patient can be stratified and evaluated for prognosis.
Specifically, the invention provides the following technical scheme:
the present invention provides markers for thyroid nodules comprising a combination of one or more of the following N-sugar chain markers: CFa, A2Fa, A4G, A4S, A4F0S, A4L, A4E, A4F 0E. The expression quantity of any one of the N-sugar chain markers has obvious difference in a healthy human body and a thyroid nodule patient, the AUC value of each sugar chain is more than 0.9 when each sugar chain is characterized in that the health and the nodule are distinguished, the specificity and the sensitivity are high, and the healthy human body and the thyroid nodule patient can be distinguished more accurately. The above N-sugar chain markers were used in combination, and the AUC value was 0.9854, the sensitivity was 93%, and the specificity was 96%, and the accuracy, specificity, and sensitivity of identification were further improved than when each sugar chain feature was used alone.
The present invention also provides, in addition to the above N-sugar chain markers, other N-sugar chain markers characteristic to thyroid nodules, which include: CA4, A2FL, A4FE, A4F, A2LF, A4F0G, A2L, A4F0L, A2 GL.
Thus, the markers for thyroid nodules provided by the present invention include a combination of one or more of the following N-sugar chain markers: CA4, A4F, A2LF, CFa, A2Fa, A4G, A4F0G, A4S, A4F0S, A2L, A2FL, A4L, A4F0L, A2GL, A4E, A4FE, A4F 0E. The expression level of any one of the N-sugar chain markers has obvious difference in a healthy human body and a thyroid nodule patient, the AUC value of each sugar chain is above 0.85 when each sugar chain is characterized in that the healthy and the nodule are distinguished, the specificity and the sensitivity are high, and the healthy human body and the thyroid nodule patient can be distinguished more accurately. The above N-sugar chain markers were used in combination, and the AUC value was 0.9944, the sensitivity was 100%, and the specificity was 94%, and the accuracy, specificity, and sensitivity of identification were further improved than when each sugar chain feature was used alone.
The present invention also provides, in addition to the above N-sugar chain markers, other N-sugar chain markers characteristic to thyroid nodules, which include: A4L0F, A2FSG, A4FGS, A4F0GS, A4FGL, A4 FGE.
Thus, the markers for thyroid nodules provided by the present invention include a combination of one or more of the following N-sugar chain markers: CA4, A4F, A2LF, A4L0F, CFa, A2Fa, A4G, A2FSG, A4F0G, A4S, A4F0S, A4FGS, A4F0GS, A2L, A2FL, A4L, A4F0L, A2GL, A4FGL, A4E, A4FE, A4F0E, A4 FGE. The expression level of any one of the N-sugar chain markers has obvious difference in a healthy human body and a thyroid nodule patient, the AUC value of each sugar chain is above 0.7 when each sugar chain is characterized in that the healthy and the nodule are distinguished, the specificity and the sensitivity are high, and the healthy human body and the thyroid nodule patient can be distinguished more accurately. The above N-sugar chain markers were used in combination, and the AUC value was 0.9989, the sensitivity was 100%, and the specificity was 97%, and the accuracy, specificity, and sensitivity of identification were further improved as compared with when each sugar chain feature was used alone.
Among the above-described N-sugar chain markers, in thyroid nodule patients, the expression levels of CA4, A4F, A2LF, A4L0F, CFa, A2Fa, A2FSG, A4FGS, A2L, A2FL, A2GL, A4FE, and A4FGE were significantly reduced compared to healthy humans, and the expression levels of A4G, A4F0G, A4S, A4F0S, A4F0GS, A4L, A4F0L, A4FGL, A4E, and A4F0E were significantly increased compared to healthy humans.
The present invention also provides a marker for thyroid cancer, which comprises a combination of one or more of the following N-sugar chain markers: CFa, A2Fa, A2L, A2 GL. The expression levels of the above 4N-sugar chain markers continuously varied among malignant thyroid nodules, benign thyroid nodules and healthy controls, and the differences among the groups were extremely significant, and the AUCs for distinguishing benign and malignant thyroid nodules of the 4N-sugar chain markers were all 0.7 or more, and the AUCs for distinguishing thyroid cancer from non-cancer controls were all 0.8 or more, and therefore, it was possible to discriminate between benign and malignant thyroid nodules and diagnose thyroid cancer.
Among the above-mentioned N-sugar chain markers, CFa, A2Fa, A2L and A2GL were significantly reduced in expression levels in thyroid cancer patients as compared with healthy humans and benign thyroid nodules.
The present invention also provides a marker for cervical lymph node metastasis of thyroid cancer, which comprises N-sugar chain markers A2LF, A3F0L, A2F0L, and A2F0 GL. The above four N-sugar chain markers are highly correlated with thyroid cancer cervical lymph node metastasis. The AUC obtained by differentiating thyroid cancer with and without cervical lymph node metastasis using a combination of A2LF, A3F0L, A2F0L and A2F0GL was 0.7148; based on the thyroid cancer markers, the combined use of A2LF, A3F0L, A2F0L and A2F0GL can be used for the differential diagnosis of thyroid cancer with and without cervical lymph node metastasis. In addition, the current method of determining whether cervical lymph node metastasis has occurred clinically before surgery is ultrasonography. The invention analyzes the AUC of whether the neck lymph node metastasis is predicted by ultrasonic examination, and the result shows that the AUC is only 0.6170, which indicates that the prediction is invalid. The four sugar chain characteristics screened by the invention are combined with ultrasound, the prediction accuracy is obviously improved compared with the prediction by ultrasonic examination, and the AUC is 0.7645.
Among the above-mentioned N-sugar chain markers, the expression level of A2LF was significantly increased in thyroid cancer patients with cervical lymph node metastasis (i.e., A2LF was positively correlated with cervical lymph node metastasis in thyroid cancer patients), and the expression levels of A3F0L, A2F0L and A2F0GL were significantly decreased in thyroid cancer patients with cervical lymph node metastasis (i.e., A3F0L, A2F0L and A2F0GL were negatively correlated with cervical lymph node metastasis in thyroid cancer patients).
The N-sugar chain marker of the present invention is named by the following references: zhang Z, Westhrin M, Bondt A, et al, serum protein N-glycosylation changes in multiple myomas [ J ]. Biochimica t Biophysica Acta (BBA) -General Subjects, 2019. Specifically, in addition to the sugar chain structure directly detected by mass spectrometry, derived sugar chain characteristics (derived sugar chain polypeptides) are obtained by Rstudio calculation using the directly detected sugar chain structure according to its structural characteristics and biological relevance. The derived sugar chain characteristics, i.e., the type of sugar chain (high mannose (M), complex (C) and hybrid (Hy) glycans), the number of antennae/branches (A), and other characteristics, such as bisected sugar chains (B), galactosylation (G), fucosylation (F) and ligation-specific sialylation (S), are calculated based on the structural characteristics of the sugar chain directly detected and the biosynthetic pathway reflected thereby. The first set of derived sugar chain features classifies all directly detected sugar chains into high mannose (M), complex (C), and hybrid (Hy) glycans. The complex sugar chain (C) is then further subdivided according to the number of antennae/branches, the presence or absence of fucosylation, the amount of galactosylation, salivary liquefaction, and the like in the sugar type. The sugar chain-derived characteristics indicate that sugar chain modification is commonly caused by a group of structurally related sugar chains. The subject of the calculation is represented by the last letter, for example, sialylation (S), to which extent the letter preceding S represents the calculation, e.g., in the fucosylated biantennary sugar chain (A2F). Thus, A2FS can be translated as a level of sialylation in the fucosylated biantennary sugar chains.
The derived sugar chain characteristics include: the number of antennas of complex N-sugar Chains (CA), the level of fucosylation (F), the level of bisected sugar chains (B), the level of terminal galactosylation (G), the level of sialylation (S), and the like. The specific structure and calculation formula of each N-sugar chain marker are shown in table 1, and when a sugar chain involved in the calculation formula is not directly detected, the sugar chain may be deleted in the calculation formula.
TABLE 1N-sugar chain marker characteristics
Note: table 1 wherein M is mannose; hy-heterozygote; t is in all glycoforms; c ═ in complex glycoforms; f ═ deoxyribose (fucose); g ═ galactose; s ═ N-acetylneuraminic acid (sialic acid); e ═ α 2, 6-linked sialic acid; l ═ α 2, 3-linked sialic acid; h-hexose (mannose or galactose); n-acetylaminohexose (N-acetylglucosamine: GlcNAc).
In the present invention, the naming, classification and derived feature naming modes for direct detection of sugar chain structures are shown in FIG. 1 and Table 2.
Table 2 sugar chain structure directly detected.
Further, the present invention provides the use of the above-mentioned marker or a detection reagent for the marker in the preparation of a medicament for diagnosing thyroid nodule, thyroid cancer or thyroid cancer cervical lymph node metastasis.
The invention also provides application of the marker or the detection reagent of the marker in preparation of a kit for diagnosing thyroid nodule, thyroid cancer or thyroid cancer cervical lymph node metastasis.
Further, the present invention also provides a medicament comprising the marker of the present invention or a detection reagent for the marker.
The invention also provides a kit comprising the marker of the invention or a detection reagent for the marker.
The medicine and the kit can be used for diagnosing thyroid nodule, thyroid cancer or thyroid cancer cervical lymph node metastasis.
The detection reagent for the marker of the present invention may be any reagent used for detection of the marker of the present invention, for example: characteristic sugar chain probe, mass spectrum detection reagent, agglutinin chip, etc.
The invention also provides a method for diagnosing thyroid nodules, which is to use the marker for diagnosis. Specifically, the expression level of the marker in the body of the patient to be diagnosed is detected, and whether the thyroid nodule is suffered or not is judged according to the change condition of the expression level.
The basis of the judgment is as follows: and if the expression level of one or more of CA4, A4F, A2LF, A4L0F, CFa, A2Fa, A2FSG, A4FGS, A2L, A2FL, A2GL, A4FE and A4FGE is significantly reduced compared with that of a healthy human body, or the expression level of one or more of A4G, A4F0G, A4S, A4F0S, A4F0GS, A4L, A4F0L, A4FGL, A4E and A4F0E is significantly increased compared with that of a healthy human body, determining that the thyroid nodule exists preliminarily.
The invention also provides a method for diagnosing thyroid cancer, which is to diagnose by using the marker. Specifically, the expression level of the marker in the body of the patient to be diagnosed is detected, and whether the patient suffers from thyroid cancer or not is judged according to the change of the expression level.
The basis of the judgment is as follows: and if the expression level of one or more of CFa, A2Fa, A2L and A2GL is reduced obviously compared with that of a healthy human body or benign thyroid nodules, the thyroid cancer is judged to be suffered preliminarily.
The invention also provides a diagnosis method of thyroid cancer cervical lymph node metastasis, which is used for diagnosis by using the marker. Specifically, the expression level of the marker in the body of the patient to be diagnosed is detected, and whether or not cervical lymph node metastasis occurs is determined based on the change in the expression level.
The basis of the judgment is as follows: in patients with thyroid cancer in which cervical lymph node metastasis occurs, the expression level of A2LF was significantly higher than that in patients with thyroid cancer in which cervical lymph node metastasis did not occur, and in patients with thyroid cancer in which cervical lymph node metastasis occurs, the expression levels of A3F0L, A2F0L, and A2F0GL were significantly lower than those in patients with thyroid cancer in which cervical lymph node metastasis did not occur.
The invention has the beneficial effects that: the expression level of the N-sugar chain marker provided by the invention has obvious difference in healthy human bodies, thyroid nodules, thyroid cancer and thyroid cancer cervical lymph node metastasis patients, can be used for diagnosing thyroid nodules, judging benign and malignant thyroid tumors and judging thyroid cancer cervical lymph node metastasis, has the advantages of convenience in detection, short time consumption, high specificity, high sensitivity and high accuracy, meets the operation requirement which a clinical diagnosis index needs to meet in the actual detection process, and can be used for early diagnosis, patient stratification and prognosis evaluation of thyroid cancer in practice.
Drawings
FIG. 1 is a graphical illustration of glycan naming, classification, and derived feature naming of the present invention.
FIG. 2 is a schematic flow chart of an embodiment of the present invention.
FIG. 3 shows the difference in expression level of 4 derived sugar chain markers among healthy controls, benign thyroid nodules and thyroid cancer groups in example 2 of the present invention, wherein the expression levels of the markers continuously change.
FIG. 4 is a ROC curve showing the distinction of thyroid benign-malignant nodules among 4 derived sugar chain markers in example 2 of the present invention.
FIG. 5 is a ROC curve showing that 4 derived sugar chain characteristic markers in example 3 of the present invention distinguish thyroid cancer from non-cancer controls (benign thyroid nodule + healthy control).
FIG. 6 is a ROC curve showing the discrimination between thyroid cancer in which cervical lymph node metastasis occurred and thyroid cancer in which cervical lymph node metastasis did not occur with 4 kinds of derived sugar chain characteristic markers in example 4 of the present invention.
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The term "glycome" as used herein refers to all sugar chains expressed in a sample (e.g., body fluid, cell, tissue) or on a particular glycoprotein.
The sample according to the invention is selected from: body fluid samples, such as blood, serum, plasma, urine, saliva, cerebrospinal fluid, lymph fluid, spinal fluid, ascites fluid, amniotic fluid; cell samples, such as cell samples isolated from tissue, cell samples cultured in vitro; tissue samples, which may be in the form of fresh tissue samples, immobilized tissue samples, and the like.
The sugar chain according to the present invention may be a free sugar chain or a sugar chain released from a glycocomplex.
Free sugar chains can be obtained using techniques known in the art, including but not limited to: enzymatic methods, for example, glycosidases, preferably the glycosidase PNGase F; chemical methods, e.g., using beta elimination reactions, glycoprotein hydrazinolysis reagents; a combination of enzymatic and chemical methods may also be used to release the sugar chains.
Derivatization methods described herein include, but are not limited to: methylamine, esterification, methylation, reductive amination, acetylation, and the like, and the type of derivatization can be selected as required. Esterification is preferred.
In the present invention, after the sugar chains are released from the body fluid protein, the N-sugar chains can be purified and/or enriched using techniques known in the art. Purification, enrichment methods include, but are not limited to: centrifugation, filtration, extraction, adsorption, capillary electrophoresis, chromatography, and the like.
In one embodiment of the invention, a Cotton HILIC SPE separation cartridge is used for enriching and purifying the N-sugar chains, wherein water is used for activating the separation cartridge, and the ratio of water to acetonitrile is 15: 85 (volume ratio) of the solution equilibrated the separation column and the sugar chains eluted with pure water.
In the present invention, sugar chain analysis and data processing can be carried out for the determination and quantification of the sugar group by analytical methods known in the art. The methods include, but are not limited to: mass spectrometry, for example, matrix assisted laser desorption ionization mass spectrometry (MALDI MS) (e.g., matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS), matrix assisted laser desorption ionization-quaternary ion trap-time of flight mass spectrometry (MALDI-QIT-TOF MS)), fast atom bombardment mass spectrometry (FAB-MS), electrospray mass spectrometry (ESI-MS), multi-stage mass spectrometry, high performance liquid chromatography HPLC, liquid chromatography-mass spectrometry (LC-MS), sugar chip technology, nuclear magnetic resonance NMR, or any combination thereof. The analysis is preferably performed by a technique with high resolution, such as MALDI MS.
In the present invention, the sugar chain analysis data is further calculated and processed to obtain desired information on the sugar group. For example, the ratio of the peak area of each sugar chain in the sample to the sum of all peak areas can be obtained, so that a relative quantitative value of each sugar chain can be obtained, deviations generated in parallel operation processes such as pretreatment of the sample and the like can be avoided, and high accuracy of analysis can be ensured; from the detected constitutional features and biological relevance of each sugar chain, derivative features including a fucose glycosylation level (F), a bisection level (B), a galactose glycosylation level (G), a sialylation level (S), and the like are calculated. These data can be directly used for relative content comparison or qualitative analysis for monitoring changes in abundance of the target sugar chain or sugar chain-derived characteristic.
The sugar chain analysis data may be further calculated and processed by analyzing the resulting data using various relevant sugar chain analysis software, databases, algorithms, and the like, and useful sugar chain analysis software includes, but is not limited to: MassyTools, Progenesis MALDI, LassyTools, GlycoWorkBench, GlycanMass, GlycoMod, GlycoFragment, GlycoSearchMS, etc. Useful sugar chain analysis databases include, but are not limited to: CCSD, GlycomeDB, CarbBank, EUROCrbDB, etc.
In the present invention, the sugar chain detection method is preferably a high-throughput detection method, for example: 96, 192, 288, 384 or more samples can be processed and tested simultaneously, which greatly reduces the time for sample preparation.
In the following examples, a blood full sugar group test was performed on 400 healthy human controls, 300 benign thyroid nodule patients, and 400 thyroid cancer patients. The queue characteristics of the samples used are shown in table 3.
TABLE 3 sample queue characteristics
The basic flow is shown in figure 2, and the concrete steps are as follows:
1. glycosidase releasing N-sugar chains
The N-sugar chains were released from the whole serum/plasma glycoprotein upstream using the glycosidase PNGase F. The method comprises the following specific steps: from each sample, 5. mu.L of serum/plasma was added 10. mu.L of 2% SDS and incubated at 60 ℃ for 10 minutes; then 10. mu.L of the enzymatic hydrolysate (containing 2% NP-40,2.5 XPBS and 1U PNGase F) was added and incubated at 37 ℃ for 12-16 h.
2. Derivatization of N-sugar chains
The N-sugar chain obtained by the above-mentioned liberation is derivatized by a known derivatization technique, and sialic acids of α 2,3 and α 2,6 linkages can be distinguished by the derivatization. The method comprises the following specific steps: mu.L of the above digested serum/plasma was incubated with 20. mu.L of derivatizing reagent (250mM EDC and 250mM HOBt in absolute ethanol) at 37 ℃ for 60 minutes.
2. Enrichment and purification of N-sugar chain HILIC-SPE
The derivatized sugar chains obtained above were enriched and purified by HILIC-SPE. HILIC uses cotton thread as stationary phase, the cotton thread is filled in 20 μ L of gun head to make purification cartridge, firstly, the cartridge is activated 3 times with 15 μ L of ultrapure water (MQ); then, the column was equilibrated 3 times with 15 μ L of 85% Acetonitrile (ACN); adding the derivatized sugar chain mixed solution into a column, and loading for 30 times to ensure that the derivatized N-sugar chain is adsorbed on the column as completely as possible; the column was then rinsed 3 times with 15 μ L of 85% acetonitrile + 1% trifluoroacetic acid (TFA) and then with 15 μ L of 85% acetonitrile for 3 times; finally, the sugar chains were eluted in 10 μ L MQ.
4. Mass spectrometric analysis of N-sugar chains
Prior to detection, the mass spectrometer was calibrated with a Peptide fragment mixture Standard (Bruker Peptide Calibration Standard II) of known molecular mass. The substrate super-DHB was dissolved in a 50% acetonitrile (water) solution containing 1mM NaOH at a concentration of 5 mg/mL. mu.L of the purified N-sugar chain was spotted on a mass spectrum target plate, and then 1. mu.L of the matrix solution was dropped on the sample and dried at room temperature. MALDI-TOF MS is used for analysis, a Smartbeam 3D laser source is equipped in mass spectrum, signal ions are collected in a positive ion Reflection (RP) mode, FlexControl software is used for control, and the m/z range is set as follows during sample detection: 1000 to 5000. The spectrogram acquisition is set as follows: for each sample point on the mass spectrum target plate, the laser completely randomly acquires signals within the range of the sample point, 10K laser shots are accumulated, and a mass spectrum is acquired, wherein the laser frequency is 5000 Hz.
5. Data preprocessing and statistical analysis
The collected mass spectra were pre-processed using FlexAnalysis and MassyTools software and exported to Microsoft Excel for further analysis. The mass spectrum data is analyzed by sugar chain analysis function auxiliary artificial analysis of GlycoWorkBench, and the identification of the sugar chain structure is mainly based on mass-to-charge ratio, secondary mass spectrum fragment attribution and published documents. The individual sugar chain quantification was obtained from the peak area of the individual sugar chain/the peak area of all sugar chains detected. In addition to the directly detected sugar chain structure, derived sugar chain characteristics (derived sugar chains) were calculated from the directly detected N-sugar chains by Rstudio in terms of their structural characteristics and biological relevance. The derived sugar chain characteristics include: the number of antennas of complex N-sugar Chains (CA), the level of fucose glycosylation (F), the level of bisecting sugar chains (B), the level of terminal galactosylation (G), the level of sialylation (S), and the like. Differences in N-glycosylation between PTC and healthy controls, between various subgroups of PTC, and the relationship between N-glycosylation characteristics and clinical parameters were evaluated by statistical tests, regression analysis, and subject work characteristic curves. The mass spectrometric data quality of the study cohort was evaluated by the standards randomly distributed on the target plate during the sample detection and calculating the mean, coefficient of variation and standard deviation of each sugar chain of the resulting plurality of standards.
6. Results and discussion
The average CV value of the sugar chains of Top30 obtained for the quality control sample was 3.17%, indicating that the data obtained in the present invention are reliable.
Example 1 markers for differentiation of healthy controls from thyroid nodules (benign nodules + malignant nodules)
96 sugar chain structures were detected in the thyroid cancer research cohort (including thyroid cancer, benign thyroid nodules and healthy controls, table 3), and 91 derived sugar chain features were calculated from the structural features and biological synthetic pathways of these directly detected sugar chains. Since the summary of derivative characteristics represents structural characteristics of sugar chains detected directly and helps explain the results and biological effects, the analysis of derivative sugar chain characteristics has been mainly conducted.
Among the 91 derived sugar chain characteristics found above, 23 derived sugar chain characteristics were significantly different between the healthy control and the thyroid nodule (Table 4). The results show that tetragonadal complex carbohydrate chains (CA4), fucosylation (CFa, A2Fa, A2Fa, A4F, A4L0F, A2LF), galactose on fucosylated and sialylated biantennary carbohydrate chains (A2FSG), sialic acid on biantennary carbohydrate chains (A2L, A2FL) are significantly reduced in patients with thyroid nodules (benign nodules + malignant nodules) compared to healthy controls; whereas one antenna complex carbohydrate chain (CA1), galactose in tetraantennary carbohydrate chains (A4G), sialic acid in tetraantennary carbohydrate chains (A4S, A4F0S, A4F0GS, A4E, A4L, A4F0E, A4F0L, A4FGE) were significantly elevated in patients with thyroid nodules (benign nodules + malignant nodules). According to the results of receiver operating characteristic curve (ROC) tests, the above-mentioned 23 sugar chain-derived characteristics were found to be effective in distinguishing healthy controls from thyroid nodules (benign nodules + malignant nodules), respectively. These N-sugar chain characteristics can be used as potential markers for thyroid nodule diagnosis. Table 4 lists the derived sugar chain characteristics differentially expressed among healthy, benign thyroid nodule, malignant thyroid nodule (thyroid cancer), descriptions of sugar chain characteristics, mean values of the expression amounts of the derived sugar chains in each group, P values for comparison of each group, and AUC values obtained by differentiating healthy and thyroid nodules using a receiver characteristic operating curve (ROC). Table 5 shows the effect of different sugar chain feature combinations to differentiate thyroid nodules (benign nodules + malignant nodules) from healthy controls.
TABLE 4 derived sugar chain characteristics differentially expressed between groups of thyroid cancer cohorts (thyroid cancer TC, benign tubercle BTN, healthy control HC)
TABLE 5 Effect of sugar chain feature combinations to differentiate thyroid nodules (benign nodules + malignant nodules) from healthy controls
Example 2 markers for differentiating malignant nodules of the thyroid (thyroid carcinoma) from benign nodules of the thyroid
96 sugar chain structures were detected in the thyroid cancer research cohort (including thyroid cancer, benign thyroid nodules and healthy controls, table 3), and 91 derived sugar chain features were calculated from the structural features and biological synthetic pathways of these directly detected sugar chains. Since the summary of derivative characteristics represents structural characteristics of sugar chains detected directly and helps explain the results and biological effects, the analysis of derivative sugar chain characteristics has been mainly conducted.
The median values of 4 derived sugar chain characteristics among the 91 derived sugar chain characteristics found above, namely CFa, A2Fa, A2L, A2GL, showed continuous changes in thyroid malignant nodules, benign nodules and healthy controls, and the difference between each group was extremely significant (t-test, p <0.0001) (table 6, fig. 3). The AUC obtained by dividing benign and malignant thyroid nodules by the 4 sugar chain characteristics evaluated by ROC is more than 0.7 (FIG. 4, Table 6), and the 4N-sugar chain markers in the humoral carbohydrate group index can effectively distinguish the benign and malignant thyroid nodules. When 4 sugar chain characteristics were combined, the discrimination accuracy was further improved (Table 6).
TABLE 6 sugar chain characteristics and their combinations Effect on differentiating benign and malignant thyroid nodules and on differentiating thyroid cancer from non-cancer controls (benign nodules + healthy controls)
Example 3 markers for differentiating thyroid cancer from non-cancerous controls (benign nodules + healthy controls)
The median values of the 4 derived sugar chain characteristics of example 2, CFa, A2Fa, A2L, A2GL, were very significantly different between thyroid cancer and non-cancer controls (benign nodules + healthy controls) (t-test, p < 0.0001). ROC evaluation of the 4 sugar chain characteristics described above resulted in an AUC of 0.8 or more for both thyroid cancer and non-cancer controls (fig. 5, table 6), demonstrating that the humoral carbohydrate group indices CFa, A2Fa, A2L, A2GL are able to distinguish thyroid cancer from non-cancer controls with high accuracy. When 4 sugar chain characteristics were combined, the discrimination accuracy was improved (Table 6).
Example 4 markers for differentiating between thyroid cancer with and without cervical lymph node metastasis
96 sugar chain structures were detected in the thyroid cancer research cohort (including thyroid cancer, benign thyroid nodules and healthy controls, table 3), and 91 derived sugar chain features were calculated from the structural features and biological synthetic pathways of these directly detected sugar chains. Since the summary of derivative characteristics represents structural characteristics of sugar chains detected directly and helps explain the results and biological effects, the analysis of derivative sugar chain characteristics has been mainly conducted.
Among the 91 derived sugar chain characteristics found above, 4 derived sugar chain characteristics, namely, A2LF, A3F0L, A2F0L and A2F0GL were highly correlated with thyroid cancer cervical lymph node metastasis (logistic regression analysis, p < 0.01). The combination of A2LF, A3F0L, A2F0L and A2F0GL, resulted in an AUC of 0.7645 (fig. 6, table 7) for differentiating between thyroid carcinomas with and without cervical lymph node metastasis, demonstrating that the body fluid carbohydrate group indices A2LF, A3F0L, A2F0L and A2F0GL are able to effectively differentiate between cervical lymph node metastasis and non-cervical lymph node metastasis. Currently, the AUC of the ultrasonic detection is analyzed to show that the AUC value of the ultrasonic detection for distinguishing whether the thyroid cancer patient has cervical lymph node metastasis is 0.6170, which indicates that the prediction result is invalid (the AUC <0.7 represents invalid), while the prediction accuracy is further improved by combining A2LF, A3F0L, A2F0L and A2F0GL with the ultrasonic, and the AUC is 0.7645 (Table 7)
TABLE 7 Effect of sugar chain discrimination between occurrence and non-occurrence of cervical lymph node metastasis
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (9)
1. A marker for thyroid nodules comprising a combination of one or more of the following N-sugar chain markers: CFa, A2Fa, A4G, A4S, A4F0S, A4L, A4E, A4F 0E.
2. The marker according to claim 1, further comprising a combination of one or more of the following N-sugar chain markers: CA4, A4F, A2LF, A4F0G, A2L, A2FL, A4F0L, A2GL, A4 FE.
3. The marker according to claim 1, further comprising a combination of one or more of the following N-sugar chain markers: CA4, A4F, A2LF, A4L0F, A2FSG, A4F0G, A4FGS, A4F0GS, A2L, A2FL, A4F0L, A2GL, A4FGL, A4FE, A4 FGE.
4. The marker according to any one of claims 1 to 3, wherein the expression levels of CA4, A4F, A2LF, A4L0F, CFa, A2Fa, A2FSG, A4FGS, A2L, A2FL, A2GL, A4FE and A4FGE are significantly reduced in thyroid nodule patients compared with healthy humans, and the expression levels of A4G, A4F0G, A4S, A4F0S, A4F0GS, A4L, A4F0L, A4FGL, A4E and A4F0E are significantly increased in healthy humans.
5. A marker for thyroid cancer, comprising a combination of one or more of the following N-sugar chain markers: CFa, A2Fa, A2L, A2 GL.
6. Use of the marker for thyroid nodule defined in any one of claims 1 to 4 or the marker for thyroid cancer defined in claim 5 or a detection reagent thereof in the preparation of a medicament for diagnosing thyroid nodule or thyroid cancer, or as a target for a medicament for treating thyroid nodule or thyroid cancer.
7. Use of the marker for thyroid nodule according to any one of claims 1 to 4 or the marker for thyroid cancer according to claim 5 or a detection reagent thereof for the preparation of a kit for diagnosing thyroid nodule or thyroid cancer.
8. A drug characterized by comprising the marker for thyroid nodule according to any one of claims 1 to 4 or the marker for thyroid cancer according to claim 5, or a detection reagent comprising the marker for thyroid nodule according to any one of claims 1 to 4 or the marker for thyroid cancer according to claim 5.
9. A kit comprising the marker for thyroid nodule according to any one of claims 1 to 4 or the marker for thyroid cancer according to claim 5, or a detection reagent comprising the marker for thyroid nodule according to any one of claims 1 to 4 or the marker for thyroid cancer according to claim 5.
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